Evidence map›Paper›PMID 32232627›Full record

Trial reportMolecular imaging and biology2020

Surface-Registration Frameless Stereotactic Navigation Is Less Accurate During Prone Surgeries: Intraoperative Near-Infrared Visualization Using Second Window Indocyanine Green Offers an Adjunct.

Steve S Cho, Clare W Teng, Ashwin Ramayya, Love Buch, Jasmin Hussain, Jessica Harsch, Steven Brem, John Y K Lee

Abstract readClinical Trial
PubMed Publisher
In one paragraph

Trial report in Molecular imaging and biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.4field-weighted citation impact, top 39% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 4 citations in OpenAlex.

  1. Review
  2. Review
  3. Article
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 1 institution in 1 country.

Steve S ChoDepartment of Neurosurgery, The Hospital of the University of Pennsylvania, 801 Spruce Street, 8th Floor, Philadelphia, PA, 19107, USA.
Clare W TengDepartment of Neurosurgery, The Hospital of the University of Pennsylvania, 801 Spruce Street, 8th Floor, Philadelphia, PA, 19107, USA.
Ashwin RamayyaDepartment of Neurosurgery, The Hospital of the University of Pennsylvania, 801 Spruce Street, 8th Floor, Philadelphia, PA, 19107, USA.
Love BuchDepartment of Neurosurgery, The Hospital of the University of Pennsylvania, 801 Spruce Street, 8th Floor, Philadelphia, PA, 19107, USA.
Jasmin HussainDepartment of Neurosurgery, The Hospital of the University of Pennsylvania, 801 Spruce Street, 8th Floor, Philadelphia, PA, 19107, USA.
Jessica HarschDepartment of Neurosurgery, The Hospital of the University of Pennsylvania, 801 Spruce Street, 8th Floor, Philadelphia, PA, 19107, USA.
Steven BremDepartment of Neurosurgery, The Hospital of the University of Pennsylvania, 801 Spruce Street, 8th Floor, Philadelphia, PA, 19107, USA.
John Y K LeeDepartment of Neurosurgery, The Hospital of the University of Pennsylvania, 801 Spruce Street, 8th Floor, Philadelphia, PA, 19107, USA. leejohn@uphs.upenn.edu.
Hospital of the University of Pennsylvania · US

Funding

Institutional Clinical and Translational Science AwardUL1TR000003 · NCATS · UNIVERSITY OF PENNSYLVANIA · PI FITZGERALD, GARRET A · 2012 to 2015
$38.9M
NRSA Training CoreTL1TR001880 · NCATS · UNIVERSITY OF PENNSYLVANIA · PI MEAGHER, EMMA ANNE · 2016 to 2025
$10.0M
NCATS NIH HHS TL1 TR001880NCATS NIH HHS UL1 TR000003
6 · The paper itself

Abstract

backgroundFrameless neuronavigation allows neurosurgeons to visualize and relate the position of surgical instruments to intracranial pathologies based on preoperative tomographic imaging. However, neuronavigation can often be inaccurate. Multiple factors have been proposed as potential causes, and new technologies are needed to overcome these challenges.

objectiveTo evaluate the accuracy of neuronavigation systems compared to near-infrared (NIR) fluorescence imaging using Second Window Indocyanine Green, a novel technique, and to determine factors that lead to neuronavigation errors.

methodsA retrospective analysis was conducted on 56 patients who underwent primary resections of intracranial tumors. Patients received 5 mg/kg ICG approximately 24 h preoperatively. Intraoperatively, neuronavigation was used to plan craniotomies to place the tumors in the center. After craniotomy, NIR imaging visualized tumor-specific NIR signals. The accuracy of neuronavigation and NIR fluorescence imaging for delineating the tumor boundary prior to durotomy was compared.

resultsThe neuronavigation centers and NIR centers were 23.0 ± 7.7 % and 2.6 ± 1.1 % deviated from the tumor centers, respectively, relative to the craniotomy sizes. In 12 cases, significant changes were made to the planned durotomy based on NIR imaging. Patient position was a significant predictor of neuronavigation inaccuracy on both univariate and multivariate analysis, with the prone position having significantly higher inaccuracy (29.2 ± 8.1 %) compared to the supine (16.2 ± 8.1 %, p value < 0.001) or the lateral (17.9 ± 5.1 %, p value = 0.003) positions.

conclusionPatient position significantly affects neuronavigation accuracy. Intraoperative NIR fluorescence imaging before durotomy offers an opportunity to readjust the neuronavigation image space to better align with the patient space.

Indexed as

Monitoring, IntraoperativeProne PositionSpectroscopy, Near-InfraredStereotaxic TechniquesCraniotomyFemaleHumansIndocyanine GreenMaleMiddle AgedMultivariate AnalysisNeuronavigationIndocyanine GreenFrameless neuronavigationIntracranial tumorsNear-infrared fluorescence imagingSecond Window Indocyanine Green

Identifiers

PMID32232627
OpenAlexW3013253239

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.